POS0346 DEVELOPMENT AND VALIDATION OF PROGNOSTIC MODELS FOR THE INCIDENCE OF RADIOGRAPHIC KNEE OSTEOARTHRITIS BASED ON NOVEL PROTEIN BIOMARKERS
Authors: R. Paz González, V. Balboa-Barreiro, L. Lourido, V. Calamia, P. Fernández Puente, P. Quaranta Díaz, N. Oreiro, C. Ruiz-Romero, F. J. Blanco
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Knee Osteoarthritis (KOA) is one of the most prevalent and disabling rheumatic diseases. The inability to detect early disease stages and the lack of effective treatments leave KOA patients without non-surgical clinical options. The early diagnosis of KOA at asymptomatic stages is crucial to improve the prompt management of the patients, in order to prevent or slow the course of the disease. Major research interest is focused on discovering prognostic markers useful to identify individuals at high risk of developing KOA before joint damage appears. <h3>Objectives</h3> To develop a prognostic model useful to predict the incidence of radiographic knee osteoarthritis (rKOA). <h3>Methods</h3> This study involved subjects without established KOA according to the Kellgren and Lawrence classification scale (KL=0-1), who were enrolled in the Osteoarthritis Initiative (OAI) cohort and the Prospective Cohort of A Coruña (PROCOAC). We developed two predictive models for rKOA incidence within 96 months of follow-up among participants in the OAI cohort. The protein biomarker candidates APOA1, APOA4, ZA2G and A2AP were quantified in serum using a custom-made multiplex protein microarray. The prospective association of the protein levels with rKOA incidence, defined as progressing to KL≥2 during 96-months follow-up, was evaluated to develop the models based on the protein biomarkers, sociodemographic, anthropometric and non-radiographic clinical measurements. Their predictive capacity was assessed using the area under the curve (AUC). The performance of the models was externally validated in the PROCOAC cohort. <h3>Results</h3> We included 602 participants from the OAI cohort in the development dataset, which had a KL ≤1 at baseline. From these, 390 did not present any radiographic signs of KOA (KL=0) in both knees. The model built exclusively with demographic and clinical data (age, sex, BMI and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score) from patients with KL ≤1 (early KOA) at baseline showed an AUC=0,719 to predict rKOA incidence during the follow-up. The inclusion of ZA2G, A2AP and APOA1 significantly improved its predictive performance, yielding an AUC= 0,793. When only subjects with KL=0 in both knees at baseline were included, the clinical model predicts the future development of rKOA with an AUC=0,724 and achieved a significantly higher AUC=0,798 with the addition of the 4 biomarkers (Figure 1). No differences were found in the predictive performance for the proposed models between the development and validation cohorts (Table 1). <h3>Conclusion</h3> Two prognostic models have been developed to predict rKOA incidence based on the measurement of four novel protein biomarkers. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.